Pennywise, but dollar foolish – What pitfalls can happen when trying to be “lean”

by Felipe Arias


The lean mentality has allowed startups to identify key issues and potential pitfalls at an early (and cheaper) stage in their lifecycle and has provided entrepreneurs with the data necessary to pursue ideas or pivot accordingly. Certainly, putting into place rigorous thinking that drives an entrepreneur to develop a vision, translate that vision into a set of testable hypotheses, identify what part of the vision is required to be tested through a minimum viable product, prioritize the tests, and then iterate through learning before spending on scaling and optimization makes intuitive sense. In practice, the drive to be “lean” can push entrepreneurs to miss opportunities, avoid key tests due to difficulty and cost, as well as misattribute the competitive advantage that will be developed by their business. Below, I include three pitfalls uncovered by those brave enough to go before us. In these pitfalls, entrepreneurs may feel that they are wisely conserving resources, but, in doing so, they are avoiding key hypotheses that need testing.

1. Not scaling sales force when appropriate. Sean Ellis introduces this pitfall in his blog post on “Bringing a Network Effect Business to Market,” wherein companies with network effects need to speed to market in order to grow the value of their network based product. David Skok hammers the point further in his series of posts on SaaS economics and building a successful sales force. However, we still see entrepreneurs who feel that they need to test one more hypothesis before scaling up. Not only do they miss the opportunity for addition revenue and margin, they may be missing out on testing other key hypotheses and gaining customer feedback.
2. Hero CEOs. Jeff Bussgang pointed out the potential to increase the efficiency frontier for an organization through applying the economic principle of comparative advantage. Sure, an aspiring CEO may be the absolute best person to gather every piece of data or even write every line of code, but leveraging the relative talents of others frees up the CEO to work on the things only he can do. One of our class guests was quick (and proud) to identify himself as a hero CEO. His talents are obvious. However, his “lean” drive to close every deal not only might have slowed down his company's growth, but also delayed him from developing a sustainable business model. As a company scales, a CEO cannot run every department, and a key part of launching a new venture is understanding when to leverage new resources.
3. Not leveraging available data and partners. The example of Mint using Yodlee vs. Cake Financial building a data resource themselves is certainly not the only one. In this example, Mint was able to move forward, test other hypotheses, and build a successful business. Cake was stuck plowing resources into developing a back end system while missing the opportunity to develop a product that truly inspired the market. In this pitfall, entrepreneurs may be trying to conserve cash, equity, or favors, but end up draining resources and missing the opportunity to build a sustainable competitive advantage. If a piece of your product is available on the market, it makes sense to not have to re-invent the wheel. In Cake’s position, they may have had to go back later to build the backend to get all the data they needed, but they could have pieced together an MVP to test quicker using the available data. Companies may feel that if I am eventually going to have to build it, I might as well avoid spending twice and start now, but, in reality, a more lean approach would be to test that hypothesis before building for scale.

Entrepreneurs in the field will certainly have more examples of when they tried to pursue a “lean” approach, but it ended up costing more in terms of time, cash, resources, and opportunities. For those looking to start ventures, it will be important to try to learn from their pitfalls. Ultimately, being lean is not about conserving resources as long as possible, it is about using them to test hypotheses as efficiently as possible.

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Smoke (and other) tests: smoke in your face? Pitfalls of lean start-up testing

by Lauren Miller

The lean start-up approach has the advantage of using genuine customer feedback as a means of improving the entrepreneur’s product specifically for those perceived as the niche consumer, but numerous stories from entrepreneurs and case protagonists about acting on false positives and negatives has caused me to wonder: can smoke and other lean start-up tests just end up being smoke in the entrepreneur’s face?” I question whether an entrepreneur should take the results of his or her tests as “gospel” since pitfalls in the experiment design and interpretation of results can be numerous. I will focus on a few of these potential pitfalls.

Focus groups
Steven Carpenter shared in the Cake Financial case that he has “learned that focus groups… can be unreliable because people can’t always say what they want until they can actually see it and play around with it.” This highlights the fact that customers often can’t or won’t tell you what they want or need and are often limited in what they can even think to ask for. It further underscores the important lessons revealed in Dropbox and Aardvark. Be careful in design of focus groups to utilize the most valuable feedback, as it is not all created equal. Also, make sure to listen to your customers but don’t simply obey them.

Usability tests
Martin Kessner’s study reveals the necessity of running usability tests iteratively. In his study, six usability teams could not find a single common usability problem when independently testing the same product, and no team found 50% or more of the total problems found by all. This should be a sign of caution to the entrepreneur. Though there are a plethora of studies and start-up case examples that document the improvement achieved from usability testing, Kessner’s work could show that in testing products or usability, the iterations make the test more reliable and decrease the likelihood of wide variations in responses. My takeaway: the repetition of usability tests allows an entrepreneur to better qualify the feedback he or she receives and in turn use it as a better determinant of when and how to pivot in hopes of achieving product-market fit.

Feature lists
I am working on an education software start-up, and we currently have a survey on our site that asks teachers which features they would like. Results have been great, as teachers seem to want all of the features we plan to create. Nevertheless, I have recently begun to question the reliability of their responses. I hypothesize that if we added fifteen more features, teachers would likely indicate that they want all of those as well, but the famous Columbia University marketing study implies that products with too many features often overwhelm customers. So how much should an entrepreneur trust customers when they say they want everything? Cindy Alvarez, formerly of KISSmetrics, says that customers want everything because “wanting” is free. Testing how much value features provide by adding a cost to additional features may yield more accurate results. If entrepreneurship is an art and a science, utilizing results of tests like this is the science and the decision of whether to follow them and how is the art.

I advocate that in the lean start-up model, focus should be placed as highly on test design and accuracy in feedback interpretation as it is on running tests. This way, entrepreneurs will avoid smoke-hazed and unreliable results that end up leading to pivots that could destroy their ability to connect with and address the needs of their potential consumer base. However, I’m interested in your thoughts. What other reliability traps have you seen entrepreneurs fall into when using various mechanisms to get customer feedback?

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Understanding When to Launch

by Katherine Nadler

For an entrepreneur, one of the most consuming decisions is determining when to launch. Arguably, a product is never complete; therefore, it is up to founders to determine what level of incomplete is acceptable. To do so they must allow their business model and market context to drive their decision making. Asking the right questions is very important.

Does the business model rely on virality? When a product anticipates customer acquisition through virality it makes sense to launch early, sacrificing some of the quality that extra time would afford. As much as entrepreneurs may convince themselves that their product is perfectly positioned to generate “word-of-mouth” or network effects, the best way to know for sure is to test that hypothesis in practice (often first through a beta launch). While the world’s become inherently social as a result of game changers like Facebook, Zynga, Twitter and Foursquare, it does not mean that products will effortlessly generate the same social response.

How damaging is a “buggy” product to my company’s value proposition? Within different businesses, the same mistakes can have very different impacts. Using Airbnb as an example, a glitch in their website where a message to an owner got lost would be annoying but would not impact the value proposition of the company. Users would still be able to find places to stay and base their opinions on that experience. On the other hand, lost files on Dropbox would be nowhere near as innocuous a mistake. This is because Dropbox draws attracts users with its claim of simplicity, reliability and safety. Therefore, when thinking about when to launch, it is important to consider the deal breakers for customers and prioritize those above all else.

What is the competitive landscape? Ventures that are entering an existing market have a completely different set of challenges from one looking to enter a new market. In an existing market, the product must be superior to those offered by established players; this can be very difficult for a resource-constrained young startup. However, in a relatively new market there might be some benefit to actually letting others enter the market first. While letting competitors grab user attention first seems counter intuitive, looking at how financial account aggregator Wesabe lost its market to Mint.com provides some interesting perspective. In a blog written by the former co-founder of Wesabe, he reflects on how “There's a lot to be said for not rushing to market, and learning from the mistakes the first entrants make. Shipping a ‘minimum viable product’ immediately and learning from the market directly makes good sense to me, but engaging with and supporting users is anything but free. Observation can be cheaper.” When entering a market where someone has gotten there first, rushing into the market prematurely with a subpar product can be very damaging while waiting can offer great rewards.

Every situation will likely have many considerations to weigh when deciding when to launch. In my opinion, founders must be careful not to jump to conclusions. Just because a founder believes in the lean startup methodology does not mean he or she should introduce a flawed product. Additionally, founders must consider all different stages of launching and realize that they have the power to separate the launch of their product from the launch of their venture in the press. At the end of the day, the best that anyone can do is make an informed decision and hope for the best.

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Can large corporations be “Lean”?

by Anonymous

I have constantly found myself thinking through the new lean startup concepts throughout this class and wondering how they could be successfully applied to large organizations.

The definition of a startup and a large corporation are completely different in every critical way: access to capital, human resources, brand awareness etc. And yet, large corporations are constantly trying to find ways to “incubate”, develop, promote and support lean startup methodologies. Over the past few years, I have seen how two large companies have tried to create a “lean” environment within their companies. The common trade-offs are:

Uncertainty vs. Scaling: Startups ideally try to reduce the amount of uncertainty through minimum viable products (MVPs), while corporations require accountable business plans and projections that force premature scaling and disincentives pivoting. Effectively there are rarely corporate structures or environments that even permit anything beyond setting a vision in the Hypothesis Driven Entrepreneurship Process. 

Testing vs. Brand: Does testing hurt a brand? As companies grow, so does their customer base, as does the size of their funnel, and ultimately their brand presence. This tension can be seen in multiple ways:

• Smoke tests are a wonderful inexpensive way to gauge customer demand for a new product, although large companies are hesitant to endorse false advertisement. 
• Constrained functionality can tarnish a brand and corporations are often unwilling to engage customers with a half baked product. 
• Frequent pivoting after a product idea has been launched at a large corporation often indicates weakness to the public and investors. In short, corporations feel they can’t afford to “fail”. 

Large companies have constantly failed at creating lean environments. As a result, they have resorted to purchasing companies for a huge premium after ideas or concepts have already proven a “product-market fit” and fail to continue encouraging lean methodology. That’s when you start seeing write-offs for early stage acquisitions that failed to grow, pivot or adapt after acquisition. Does that mean corporations can’t be lean? I would still like to believe there is hope. Corporations would need to constantly test unbranded products and cut off their reliance on branding to drive hype and validation. Most importantly, corporations must create an environment that allows and encourages failure.

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Are We There Yet: Thinking Through Product/Market Fit (LTV)

by Colin Barry

"Startups occasionally ask me to help them evaluate whether they have achieved product/market fit. It’s easy to answer: if you are asking, you’re not there yet.” – Eric Ries, “The Lean Startup” (pg 220)

The Context

Hypothesis-driven entrepreneurship — epitomized by Eric Ries’ Lean Startup methodology — has become all the rage among aspiring tech founders. It’s not hard to see why. Ries’ focus on customer discovery and iterative development addresses a dangerous problem in the two established paradigms for building software.

Waterfall product development presumes that the problem and the solution are known, and we just have to build the solution in an efficient, staged manner. Agile product development admits that the solution is unknown, but still presumes that the problem is known — the “voice of the customer” (usually the product manager) will recognize useful software when she sees it. But actually, Lean Startup tells us, the problem is usually unknown, too: it takes ingenuity, guts, and contact with customers <shudder> to determine whether software is useful or not.

The Problem

Okay, so we admit that we’re in the land of the blind, and we need to be disciplined and systematic about figuring out what problem we should be solving. But how do we know when we’ve got the right problem paired with the right solution?

Ries tells us that the watershed condition is product/market fit — we’ve looped through the Build-Measure-Learn process, improving all the while, until we’ve built an “engine of growth” (by which Eric appears to mean a product with rapidly accelerating user acquisition). Then, we just add fuel and watch the engine go.

The trouble is that a whole bunch of phenomenally successful and (purportedly) Lean startups slowed down rapid product development iteration — basically, decided that the product was mostly done — well before they had anything resembling an engine of growth.

Why It Matters

The lack of rigor around what constitutes product/market fit makes successfully applying Lean methodology much harder. One of our class guests, David Skok, has written brilliant expositions on constructing a repeatable, scalable sales model. He opened our class discussion with a graphic (roughly): 

 
But wait, my business only has product/market fit if I already have a sustainable user acquisition model — ideally an engine of growth so powerful that just a small quantity of “fuel” causes users to crash my webservers and beat down the front door to my startup’s office in desperate mad dash to consume my product.

But it seems to me that my venture has probably already figured out the marketing and sales part of the equation if that is occurring.

And many (if not most) successful startups do appear to delineate building a product from building a sales model.

Two examples in brief (one cribbed from David Skok):

- Airbnb enters YCombinator with a product they have already trialled at SXSW and the Democratic National Convention. They expect to do a ton of development in YC. Paul Graham tells the founders to stop building product, hop a plane to New York City (where they have the most adoption), and personally snap prettier photos of the apartments currently listed on Airbnb.
Scalable? No way.
Did the market tell them to do this? Nope (at least I don’t think so).
Did it work? Yes. Prospective renters and couch-surfers had been turned off by ugly descriptions of the listed apartments. Success.

- Constant Contact completes product development (in the words of David Skok, has found product/market fit). But prospective customers aren’t buying.
In fact, prospective customers (small businesses in Atlanta) aren’t sure why they would need mass-mailing software in the first place. Constant Contact distributes wire-bound books for small businesses to use as visitor books to record shoppers’ e-mail addresses.
A month later, Constant Contact’s prospective customers have hundreds of e-mail addresses, and now they need mass-mailing software. Success.

We can tell similar stories for startups like JBoss, RentJuice, and Dropbox.

To be clear, I don’t think David is wrong in making a distinction in the startup lifecycle between product development and marketing/sales. I think Lean Startup makes it difficult to tell when to stop focusing primarily on building product and start focusing primarily on selling product.

Conclusions

One possibility (maybe what Eric would argue) is that I’ve defined the “product” part of “product/market fit” too narrowly. The product is not just a piece of software but rather the startup as a whole — a product-marketing-sales-PR conglomeration that must be tuned and operating in harmony.

I’m not so sure. There is a natural tension between Lean-style product development and marketing/sales.

In Lean product development, we tirelessly labor to determine what the customer will use and then build something viable and desirable. If the customer tells us (by his or her behavior) that a feature should be different, we change. We pull the product out of the market.

In sales and marketing, we tirelessly labor to convince the customer that they should use or buy what we already have. If the customer tells us that a feature should be different, we generally try to convince him or her that we know better. We push the product onto the market. And ideally, the customer doesn’t require much convincing.

Lean Startup methodology needs a better definition of product/market fit than “I know it when I see it.” Maybe arbitrary standards like 40% net promoter score make the most sense. Perhaps we just admit that user acquisition growth is not the right metric for many startups, especially those that require higher-touch sales models. Regardless, I think we need to take a deeper look at what goal we’re actually chasing if we’re running lean.

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Don’t Scale Until You Have Validated Your Business Model?

by Jonathan Lo

A common theme in the Launching Technology Ventures class is how to utilize a “lean” model until one has validated the business model. Companies such as Dropbox and RentJuice are examples that did precisely that to achieve success, while a company like Cake Financial is openly criticized for not following those guidelines. While I do generally believe in the lean start-up principle, some of the most successful start-ups such as Google, Youtube, Facebook, and Twitter, blatantly ignored this principle to become the companies that they are today. These companies focused on achieving strong network effects before they had any clear plans for monetization. Is this the right approach?

I am involved in a start-up called SaferTaxi, a company that is developing a smartphone application to allow for the booking, paying and rating of taxis in Latin America. While SaferTaxi plans on being one of the first movers in Latin America, this is by no means a new concept in other more developed regions of the world. Companies such as Uber in the US, gettaxi and mytaxi in Europe, have all received lots of Venture Capital funding to achieve scale within their respective regions. While there has yet to be a dominant player in the taxi booking space, initial data provided to investors have indicated that there is a lot of potential for monetization. Are these company’s successes enough to validate our own business model?

A good number of Latin American start-ups were able to achieve success by taking concepts from “developed” markets and implementing the same concepts faster than the incumbents. A great example of this is Mercadolibre, an Argentine company that implemented and scaled the eBay model throughout Latin America. While there were other local competitors that launched around the same time, Mercadolibre was able to scale quickly and gain traction before any of the other players (including eBay). Is this the model we should be following in Latin America?

SaferTaxi is currently faced with many of these questions. While following the lean start-up model is an intuitive path to validate an entirely new business model, are the successes of the likes of Uber and gettaxi enough to validate the SaferTaxi business model? Other start-ups with the same vision as SaferTaxi have already started to emerge in Argentina, Brazil and Chile. Should SaferTaxi be focusing on refining the business model using lean principles and worry about competition after a superior product has been created? Or should SaferTaxi be focusing more on a land grab before it is too late to enter certain markets within Latin America?

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Warning Label

by Joshua Chuang

Hypothesis-driven, lean entrepreneurship can be the difference between a successful venture and a failed one. This strategy requires one to propose a hypothesis, develop a test around the hypothesis, test it, and learn from it. At its core, hypothesis-driven, entrepreneurship strives to reduce the biggest risk startups face: building a product that no one wants. A powerful tool for entrepreneurs, but potentially dangerous for those who don’t fully understand it or who overlook certain dangers.

The following serves as my warning label to the entrepreneurs out there following the lean startup methodology:

1. BEWARE of AssumptionsPeople tend to presume they understand how things work, when in fact they often don’t. For example, with Cake Financial, the founder and CEO, Steve Carpenter, assumed that the user-interface wasn’t what people cared about and focused his attention on the back-end. He also assumed that Yodlee would be a bad business partner, based on his prior experience. Both are completely valid and, seemingly, reasonable points. However, other plays (like Mint) were able to capitalize on Yodlee’s abilities to create a strong product. Don’t make assumptions/presumptions! Whenever possible, test your assumptions.

2. BEWARE of false positives and negativesConsider the following scenario. You’re friends with many people in the VC and tech industries. You make an assumption that people in this world want a product that automatically folds your laundry (I hate folding clothes, one of you should really make this product). Your friends all love it, and you’re receiving some good press from some famous blogs. All signs point towards making the product, right? WRONG! This is a false positive! While it could be true, it’s not a definitive positive (and you should never read it as one). One might begin building an expensive prototype when, in fact, there was no real demand. So what should you do? When designing your test, create a checklist of potential outcomes and their associated implications. Could I be receiving good press simply because I’m connected to the right VC firm or right advisory board? Are my friends supportive because they believe in me or because they believe in the product? Next time you hypothesis test something, make a checklist first.

3. BEWARE of the “Customer knows best” mentalityPart of the lean startup methodology requires creating minimal viable products, testing them, and improving them based on feedback. Learn as much as you can as quickly as you can. However, what happens when people start asking for numerous improvements/features? What happens when users fundamentally dislike one of your key features? I wish there were a simple rule as to when you listen to the customers and when you stick to your beliefs. Like many things in life, the answer is “it depends”.

In short, my advice is to take your “belief” and test the crap out of it. If the feedback says make changes, but you still wholeheartedly believe you’re right, then try and figure out why they’re wrong. Ask those you trust whether your reasoning makes sense. As I’ve already established, every entrepreneur likes to believe they know best. What I’m telling you is to never trust your gut alone. Prove it if you can. And if all else fails, then make a choice and pray it works out.

Good luck entrepreneurs. You’ve been warned!

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Bridging Two Classes

by Douglas Romanoff

My final semester at HBS was a unique experience studying the business models of technology‐based  ventures from two different perspectives. From late March to May, Competing Through Business Models (CTBM) class with Professor Hanna Halaburda equipped me with frameworks for how firms create and sustain competitive advantage through their business model designs. From January to early March, Launching Tech Ventures (LTV) class with Professor Tom Eisenmann exposed me to the approaches entrepreneurs use to erect attractive and scalable business models from scratch. 

Despite the clear interrelatedness of the two topics, the content of the courses could not have been more different. CTBM defines a business model as the logic of a firm, the way it operates to create and capture value for its stakeholders. Expressed as a set of policy, asset, and governance choices (and the consequences derived from those choices), business models are most effective when they support virtuous loops of value creation aligned with a company’s mission. The diagram below captures a simplified representation of the business model for Microsoft’s operating systems and productivity applications business. Here, management decides to set low prices for its operating system, set high prices for its applications, and invest heavily in R&D for next generation operating systems, putting in motion a series of consequences that are self‐reinforcing and defensible, creating a sustainable business model. 

Source: CTBM Introductory Note, Jan. 2011

In the world of CTBM, analytical techniques such as game theory and classic optimization are the tools of choice for decision‐making. Changing the pricing scheme is about finding the Nash Equilibrium. Augmenting the product portfolio is about evaluating and optimizing the payoff matrix. The CTBM toolset must be familiar to any entrepreneur building a new business model in practice, right?

Absolutely not. Instead, we see in LTV that the Lean Start Up Methodology is the approach preferred by  many experienced entrepreneurs and venture capitalists. According to this methodology, entrepreneurs should not try to abstractly design the perfect business model in a top down fashion. Rather, entrepreneurs should launch stripped‐down versions of key business model elements piece‐by‐piece, building up gradually. And entrepreneurs should not seek to predict what will work based on purely analytical models. They should leverage “hunch” to develop working hypotheses, then rapidly iterate through cycles of trial and error. Successful business models are more often discovered or nurtured rather than designed in their entirety and set in motion with a few tactical decisions. 

So why the fundamental disconnect? The answer seems to lie in the fact that business model design from scratch is a messy undertaking. The number of decisions that must be made is often astounding. The interactions between various sets of choices are often complex. Competitive dynamics are constantly altering the viability of certain approaches. In situations of such uncertainty, designing a business model is a highly strategic exercise that does not lend itself to white‐boarding in a vacuum or deriving purely analytical solutions. Instead, one increases the likelihood of success through a balance of other factors. Experience accumulated over time provides the intuition to develop accurate hypotheses about what works and what does not. Experimentation provides a constant stream of market feedback to update and refine initial assumptions.

The Lean Start Up Methodology works because it prescribes how the entrepreneur should apply these factors in a manner that best conserves cost and risk. The CTBM toolkit, in contrast, is best applied in situations where the uncertainty is more manageable—e.g., to identify incremental improvements to business models that are already in operation. Here, the interactions between choices are better understood, and the range of outcomes more limited. In such a case, decisionmaking takes place at the tactical (not the strategic) level, making analytical approaches more tractable.

The Lean Start Up Methodology is less appropriate in such a situation; while experience and experimentation are still fundamental elements of the business model design process, analysis rises in importance as the scope of uncertainty becomes more manageable. LTV and CTBM each provide frameworks for addressing the challenge of designing business models for technology companies. Contrasting the two clarifies the context in which each toolkit is appropriate.

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The Science of Business and the Business of Science

by Douglas Romanoff

Eric Ries champions the Lean Start Up Methodology as a means for entrepreneurs to more efficiently manage financial and human resources while increasing their odds of success. At the root of his approach is the idea that entrepreneurs should measure progress in units of validated learning, rapidly iterating through build‐measure‐learn cycles designed to test hypotheses and refine business model elements. By avoiding excessive subjectivity, and instead gathering empirical and measurable evidence, the entrepreneur operates with less risk and more cost effectiveness.

Although Toyota’s management philosophy is credited as the inspiration for lean concepts, the true origins of the approach lie in a discipline outside the realm of business. The Scientific Method has served as the gold standard of systematic inquiry for chemists, biologists, and physicists over many centuries. According to its tenets, researchers should gradually refine elements of a scientific model through a series of structured experiments, obtaining measurable data to validate clearly articulated hypotheses. Sound familiar? The researcher’s scientific model is the entrepreneur’s business model; the researcher’s test tube, the entrepreneur’s Minimum Viable Product (MVP). So Eric Reis’s breakthrough is not in the creation of the Lean Start Up Methodology itself, but rather in borrowing the method from science and applying it to entrepreneurship, reframing its underlying concepts in the nomenclature of business. His insight is that both cutting edge entrepreneurship and cutting edge science are experimental endeavors, and can share techniques to investigate poorly understood phenomena, acquire new knowledge, and integrate that new knowledge to refine previously held beliefs.

If we agree that Lean Start Up Methodology originates in the techniques of science, there is irony in the notion that it does not apply to science‐driven businesses. In his blog, Union Square Ventures partner Fred Wilson argues that there is a growing cleavage in the venture capital industry between businesses such as software and businesses such as clean technology. Many students in Launching Tech Ventures would seem to agree, arguing in class that lean approaches do not apply to startups involving substantial technical risks and manufacturing assets. Fast product development cycles are not practical and customer feedback is less relevant for such business, they might add. In brief, the science of business does not appear to apply to the business of science.

I argue that this view is by and large misguided, though not altogether without its merits. The first fault in such a line of thinking is definitional. Cleantech is not a monolithic and uniform industry of its own, but rather an umbrella term that encompasses energy generation, transportation, telecommunications, and other verticals. Each of these verticals supports an array of business models with very different economics. In fact, there are subsectors and segments of the value chain in cleantech that are more similar to information technology, services, or electronics than not—consider OPower, SunRun, and Enphase. All lean concepts that apply in these more familiar industries apply to related spheres of cleantech. Comments about the applicability of the Lean Start Up Methodology to cleantech businesses therefore err by generalizing to broadly.

In keeping with the underlying spirit of such arguments, however, let’s focus on a subset of cleantech that is driven by science: energy storage. Battery manufacturing involves the technical risks and physical assets described earlier, and as a result does not lend itself to rapid iteration to the same extent as, say, developing brokerage software for real estate agents. Upon closer inspection, however, this line of thinking confuses cheapness with leanness by using an inappropriate benchmark for cleantech. The Lean Start Up Methodology does not promise to level the playing field between Aquion and RentJuice with regards to funding requirements. A new software application is in most cases cheaper (e.g., requires less capital) to develop than a new battery chemistry. Game over.

But being lean is not about being cheap—it’s about being efficient with resources. By applying the concepts of the Lean Start Up Methodology, Aquion has more efficiently used its resources while increasing its odds of success relative to other battery start ups operating under the status quo. Founders Jay Whitacre and Ted Wiley stage technology development through a series of MVPs (e.g., bare bones R&D solutions that are “good enough”) designed to validate key hypotheses (e.g., potential product performance and manufacturability) with the smallest set of product features (e.g., precision only to the degree that is necessary, and no more). The result is rapid iteration by battery development standards and greater capital efficiency relative to peers in the same industry.

Clean technology and leanness to are two concepts that seem to invite misunderstanding and misuse. By applying them both with greater precision, I believe we reveal more clearly where the one relates to the other.

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Lean is for Wimps

by Lorin Pace & Iris Guerra

In the new era of all things lean, fat gets a bad rap. Even the terminology is loaded; in the U.S. we are facing an obesity health crisis like nothing we have faced in our nation’s history. Of course no-one would want to be ‘fat’ when the term has such a negative connotation. But when did ‘fat’ become the only alternative to lean? What about medium or athletic builds? Painting a picture of two polarized options and demonizing the other is a storied psychological tactic for building momentum around your own philosophy. For better or for worse, Eric Ries has done a great job of depicting epic failure as the product of the ‘other’ approach. And he has a point. It IS senseless to build a product no one wants, and no one is a better example of that than Ries himself (he did it!) and he knows how painful it is to pour your heart and soul into something that ends up being discarded. Ries would have you believe that not only can you apply the lean startup method to everything – but you should apply the lean startup method to everything.

One of the cornerstones of Ries’ lean startup method is the notion of the Minimum Viable Product (MVP). In Ries’ own words, “The minimum viable product is that version of a new product which allows a team to collect the maximum amount of validated learning about customers with the least effort.” It sounds like a helpful, leveraged approach, and it is, but we’d like to slap a warning label on this product:

1. Don’t prioritize validated learning by gambling with key customer relationships. We found rather quickly that warm leads are absolutely critical for winning the business of large enterprise customers, regardless of how far along your product is. Warm leads with large enterprise customers in ideal segments are rare. Even if you have a brilliant concept, while you’re learning about what these customers truly value, you are exhausting much of their valuable time. Don’t expect them to hold their breath while you quickly iterate on the product that you figured out that they actually do want. They may not have the patience to re-engage with you. Had you been more prepared, you might have just landed a huge customer. 
2. If you are constantly validating your gut, the guy who doesn’t may beat you to market. In the wake of the Bush presidency and the ultimate failure to unearth weapons of mass destruction (WMDs) in Iraq, the notion of the ‘gut’ is almost as unpopular as ‘fat.’ Trusting one’s ‘gut’ is synonymous with making whimsical decisions based on mood and temperament. The reality is quite different. The ‘gut’ refers to the part of our brain known as the ‘limbic system.’ The limbic system assembles powerful elements of memory and feeling and association that can distill complex patterns of information into a singular decision path. It can be very powerful and trusting such hunch-driven decision making has produced many of the greatest successes in the history of entrepreneurship. It might not work every time, but startups rarely do. Overly handicapping your gut with too many feedback loops can be just as risky as the alternative.

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Lean Startup Principles and Services Businesses

by Whitney Baxter (@whitneybaxter) & Dave Krasik (@davekrasik)

Our project focused on building out the sales and marketing strategy for a growing tech‐focused service firm. We attempted to apply many of the LTV principles to this service model and found them quite useful. Some we found could be applied interchangeably with between product and service based firms, while other required modification or adjustment. We’ve focused on the later for our post, but in both instances the principals were useful lenses through which to examine the business.  

We’ve observed that services firms can successfully apply some of the most important concepts of lean startup methodology, including minimizing startup costs and iterative hypothesis testing.

Resource Constraints

Launching a service firm can require significantly less capital than launching a product firm but may be much harder to scale at the high rates achievable by product firms. The costs of resources to create lean product‐based startups are becoming increasingly less prohibitive. Launching a product firm requires fewer owned and captive resources. New tools, like Amazon Web Services, allow resources to be leased and utilized on‐demand helping to reduce the equipment and employee startup costs. Online communities and tools enable founding teams to modularize their product or engineering development processes and increasingly leverage a global talent pool.

While the same dynamic holds for services firms, it is most applicable to their ancillary tools or overhead costs. For most service firms, the customer evaluates the capabilities of founders directly when making an economic decision. In product firms, the founders are indirectly evaluated through the products they create. Logically, service firms are more constrained by their founders’ time and capabilities rather than monetary and tangible resources. As a lean service‐based startup begins to scale, it becomes more resource constrained by the founder’s customer acquisition and engagement time. Product based firms can scale to astronomical levels by fueling their marketing and engineering efforts with more money. The fuel of service‐based firms, talent, can be much harder to acquire and rapidly integrate.

Service‐based startups can also have more financing flexibility than product‐based startups. In a recent blog post, Mark Suster recently addressed the types of financing applicable to service firms: angel, bank, “customer,” and vendor.1 Because service firms are less constrained by money (i.e. revenue is primarily generated by founder time while most other costs to serve are relatively small) they are less beholden to certain forms of financing (high risk, reduced control VC money).

Hypothesis Testing

For services firms, a lens of market need / internal competency / passion can be a helpful evaluation tool. Using lean principles, service‐based startups can test hypotheses to better gauge opportunities and tradeoffs within this framework. Service firms can be as flexible as the talent that they bring to the table. This allows them to reach out quickly to new market needs and learn new internal competencies, but it also requires them to account for potentially constraining motivations of their employees.

While selling services can be much easier than selling B2B products, feedback from the selling process can often be less conclusive and appear more slowly. Engagement and selling processes are slow and the custom needs and expectations of clients can vary widely. A broad testing campaign is often required, as service‐ firms often want to avoid overspecialization at the outset. A service‐based firm’s internal competency is refined with repetition, like product‐based firms, but customization in service offerings can require more exhaustive testing and leads to less conclusive results. As a result, it can be more difficult to identify your customer within the market and your evangelist within firms.

Scaling a Tech Services Firm

Concurrent with a services firm’s efforts to test different internal competencies, a firm must also efficiently scale into new client markets. New markets allow for a broader set of revenue streams and defend a firm against pigeonholing and obsolescence. Firms that fail to extend into new markets may see their niche become commoditized or hyper‐competitive while more flexible firms continue to grow as market demand shifts.

Referral Rings

Due to the high importance of referrals and references in the relationship‐based customer acquisition process for service firms, it is useful to view scaling in terms of network rings. The inner ring consists of a firm’s initial professional contacts. In the case of our project, this would be the founders’ academic network and the first companies to which this network connected them. The next ring consists of companies and decision makers to whom the inner ring can provide referrals.

Referrals and reference to completed projects significantly reduce a client’s perceived risk and customer acquisition costs.

Grabbing a Beachhead

In light of a new client’s risk perception, there are two additional effective techniques for extending into a broader client base. First, a firm should lead with their strength. If a firm has built a credible portfolio in a particular competency they should initiate their relationship with a new client through this competency. There are several advantages to this technique:

1. The firm already knows how to sell the service. They’ve given successful pitches before. They know the key decision points and they know the key decision makers. 
2. The firm can reference similar projects from their portfolio, outline expectations, project ROI, and refer to past clients. 
3. The firm already knows how to execute. They are not learning on the fly. They know the potential pitfalls and can quickly overcome them. This presents a credible first impression that the firm is able to execute on the services that they sell. 

The next technique is to give away new services, provide discounted services, or provide services with the option to pay following completion. The advantages include:

1. Reduced financial risk for the economic buyer. 
2. Increased project cycle time because lower and zero cost projects require less red tape. This can speed up iteration and the learning curve. 
3. Build deeper relationships with key decision makers. This informs future sales pitches and can improve the operational success of follow‐on projects. 

Both techniques aim to create beachheads within new client bases. As beachheads are successfully created, the firm can expand to greater project scope and scale within each client organization. As they scale, firms can use these techniques to build new competency portfolios that again allow them to extend to further network rings and clients.

Branding: Perception and Timing

Another important consideration when scaling involves branding. Young firms are generally valued for their technical competency and ability to execute on an RFP. Due to lack of experience a firm is often viewed by the economic buyer in terms of the ROI that its’ services provide for each one‐time engagement. Attempts to sell based on brand have little impact on the buyer. As firms mature, broaden their portfolio, and generate a proven track record, they begin to build credibility in the marketplace. At this point they can begin to build a brand that aligns with their reputation and existing portfolio. This branding can become a strong differentiator as they grow, markets mature, and new competition enters the marketplace.

While external strategy is expressed through branding, internal strategy is represented by the processes and objectives that the firm intends to carry out within their own organization. In our firms case, they attempt apply IDEO‐style design concepts to their engagement process. While they have seen these as successful methods for creating solutions that their clients highly value, they have noticed that clients do not have an appreciation for the methods or internal strategy that the firm uses. Attempts to sell this external strategy are at present unproductive but this does not mean that the firm should deemphasize the design concepts in their internal strategy. These indeed could become the foundation of their brand strategy as the company matures.

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Learnings from Applying “Lean Startup” to a Science-Based Business

by Arun Agarwal (Twitter: @arun_agarwal)

Recently I had the opportunity to take Launching Technology Ventures with Tom Eisenmann at the Harvard Business School, and learn the latest and greatest about the process for building capital efficient startups using the “lean methodology.” Lean is a movement started by Eric Ries that encourages entrepreneurs to design cheap experiments to test their products and positioning in the marketplace and get real data, rather than relying on the founder’s grand vision which can often lead to building a business that is 10, 90, or 180 degrees off from actual market needs.

My project for the course involved a “science-based” hardware business. I worked with a university professor in Switzerland to spin a fundamental technology out if his lab that we believe could revolutionize on-chip and off-chip digital communications. Tools and techniques that lean methodology suggests using include highly agile product development cycles, launching early, building “dummy features” to see if users interact with them, A/B testing, and watching customers actually use your product. As such my initial reaction was that lean had no place in the business I was working on, where our customers will in many case be large semiconductor or hardware manufacturers (not a group that can easily be used in a beta test), and our product development cycles are long (since we’re doing fundamental research in a lab and fabricating something physical vs. writing application code in an Amazon EC2 cloud).

What I learned however, was that even though lean will need to develop a different set of tactical recommendations about how to run a product development or marketing process in such businesses, the underlying framework is still very useful for entrepreneurs in this space:

• Develop a hypothesis for what you believe the right answer will be. 
• Design a cheap experiment that serves as a falsifiable test to see if your hypothesis can be disproven. 
• If the answer is “it can be disproven,” then reform the hypothesis and re-test it. Otherwise, form a new set of hypotheses that further your understanding. 

The key here is appropriately designing the falsifiable test so with some degree of certainty you can challenge your hypothesis, rather than just “gathering data” through market research to give yourself a better hunch. This guidance helped me and my colleagues ask specific questions of market experts such as “is there anything that would make it impossible for 5 engineers and a $4M capital base to develop XYZ product for ABC market in 2 years?” The response we heard was “well before you could start development, you would need cross-licensing IP agreements with either company D, E, or F, and such an agreement typically takes at least 10 months to put in place.” 

This helped us understand that we couldn’t simply solve the problem of hitting market at what we believed was the right time by doubling the number of engineers or capital, but that it was a long pole external item. From there we could ask the next set of questions such as, “how would we go about finding the person we need to get on board to get the deal done in 4 or 5 months?” (a new hypothesis that such a person must exist). This iterative process helps us systematically remove risk from the business without spending a lot of money.

I encourage people who are passionate about the lean methodology to develop more specific techniques for deep technology entrepreneurs so that everyone doesn’t have to design their own experiments from scratch. While I have great belief in the future of Internet businesses with strong network effects to both change the world and return well for their investors and operators, I feel it’s undeniable that revolutions in clean technology, biotechnology, and data infrastructure have a critical and unique role to play in the sustained growth of entrepreneurship and United States GDP.

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Lean: Why Now?

by Christophe Mandy

Class cases on Aquion Energy and Predictive Biosciences were chosen specifically to illustrate that the lean concepts don’t just apply to the web-based consumer facing startups that the paradigm is usually associated with. In both cases, running lean involved the same kind of hypothesis testing, pivoting and search for product-market fit, just on a different timescale and with a different context. But if the lean principles aren’t enabled by the sort of economics that underlie internet companies, why did the concept only arise now? The “eliminating waste” Toyota-Production-System-like ideas in manufacturing are more than 40 years old and were all the rage almost 20 years ago.

One possible answer would be to suggest that ideas in management theory for startups hadn’t evolved enough until the last ten years, and that nobody had thought of generalizing success stories into the key lean principles. This implies that applying lean principles would have always led to higher successes rates for startups. My sample set of startups to study is small and biased, but when I go through our first year TEM cases, it usually requires an uncomfortable stretch of lean-principles to try to discern lean startups in any pre-1995 case.

Another answer would be that the extrinsic conditions that surround startups have changed and evolved, and give lean startups an edge now and only for a limited amount of time (in the same way, for instance, that American conglomerates created value in the 60s in a way they no longer do, or that relentless pursuit of learning curves gave an edge to companies in the 70s). A startup has to expend resources for a limited set of activities such as product/service development, order fulfillment, hiring and training human capital, marketing, etc…  Basically, scaling only when a startup has proof that it has achieved product-market fit, and religiously refusing to spend resources on any activities that don’t increase the chance of product-market fit is only possible because the set of activities around customer interactions have become cheap relative to other activities. Lean works on web-based consumer facing startups in part because when the product is code-based (very cheap to change), or meets simple needs of consumers (making a mistake with one customer won’t cause memorable ripple effects),  it’s possible to market to a set of a target customers, integrate learnings from small-scale experiments and adjust features in a product with much fewer resources.  All of this is also possible because a very small marketing budget spent judiciously on Google ad-words and clever blogging can create traction in a market. If these activities were expensive relative to, say, hiring and firing employees, then getting to product-market fit by taking risks, making mistakes and correcting them would take a background role to trying hiring and firing as many employees as possible and until the right team is formed.

Trying to understand why lean only appeared now is more than an academic exercise. If it really is all about the relative costs of startup activities, then lean will have to adapt in the coming years. When creating high quality original content on a blog will no longer be sufficient to generate buzz because readers are overloaded with information, or when bidding on ad-words drives the prices on any sensible online advertising too high for startups, being “lean” will no longer be tied to getting to product-market fit fast. What will come next? My guess is that SaaS will make the activities around scaling relatively cheaper than others: a company of size will have access to the same kind of highly automated high quality ERP/CRM/SCM software that only large enterprises use now. And as much as it may sound anathema now, maybe the emphasis will shift to achieving scale first, and fixing the product later?

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Missing Paradigms

by Christophe Mandy

The first half hour of every LTV class follows a similar pattern. We invariably spend some time discussing whether the company under scrutiny truly is lean startup and evaluating where on the LEAN-NOT LEAN scale we should place it, and right around that time we implicitly (and in one case explicitly) assume that although the lean framework is elegant and concise, it should be taken with a grain of salt, and the success or failure of a company does not depend on its dogmatic application, nor do all companies apply each principle consistently. Invariably, somebody compares the company under scrutiny to a highly successful enterprise that was manifestly not a lean startup (traditionally Google, Zynga or Facebook).That DropBox ignores some of the customer feedback it gets, or that IMVU waits until it gets no traction or income before actually paying attention to customer feedback makes neither more or less of a lean startup than the other. So this simple evaluation begs the question: is the lean framework a valid one dimensional tool? Are there more paradigms than “lean” and “not lean” (Fat? Opulent? Ostentatious?).

I couldn’t possibly come up with a taxonomy of every possible kind of framework a successful startup can fit in, and isolate the conditions under which a startup should apply each one. And I don’t really know the history of that many startups. But I could try to do a full factorial test of each lean principle to see if they’re necessary. We basically defined lean startups as doing the following:

A.  Getting to market as fast as possible

B.  Testing hypotheses about the product and monetization to validate them based on explicit metrics, then pivoting until product-market fit is reached

C.  Scaling only when product-market fit is reached

Testing the principles looks like this:

	A: Get to market fast		notA: hide from customers
	C: scale post-PMF	notC: scale pre-PMF	C: scale post-PMF	notC: scale pre-PMF
B: Test hypotheses and get to PMF	LEAN	Facebook	Google	Movie studios
notB: No tests	Sony Walkman	Market-share-now = money-later philosophy (Samsung?)	Apple iPad	Honda entering the US

It’s doubtless arguable whether or not I’ve put the right companies in the right boxes and if each is really an example of a success story, but that’s not quite the point of this exercise. I’m sure you can also tell that filling every box didn’t come that naturally. And it’s not necessarily surprising that there can be success stories in each category. But looking for simple patterns suggests that:

·     notA companies are based on monetization that heavily benefits from making a big splash when a new product is launched (not conducive to viral spreading for instance),

·     notB companies seem to not live in markets that might tip whereas B companies face the threat of winner-take-all situations,

·     notC companies are in industries where there is extremely intense competition and the possibility of low margins

So is lean only good for companies that could spread virally in markets that don’t tip with not-too-intense competition?

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